- Assess spatial as well as temporal variation in disease prevalence
- Identify risk factors, areas of high risk
- Allocating resources efficiently
2020-08-01
Onchocerciasis prevalence map
Onchocerciasis prevalence data on different decades
Different diagnostic test in different decades
Raster layer for some of the predictors
| 2.5 % | 97.5 % | ||
|---|---|---|---|
| (Intercept) | -278.3242328 | -455.1229757 | -101.5254900 |
| alt | 0.0169194 | -0.0050694 | 0.0389083 |
| isothermality | 2.1396189 | 0.6583329 | 3.6209048 |
| temperature.seasonality | 0.0016484 | -0.0198791 | 0.0231760 |
| annual.precp | 0.0212815 | 0.0083844 | 0.0341786 |
| popden | -0.2144603 | -0.8037939 | 0.3748734 |
| annual.mean.temp | 0.3790577 | 0.0113506 | 0.7467647 |
Predicted prevalence map by Generalized Linear Model
Sample variogram with different parameters
Wave variogram fitted for Ethiopian prevalence data
Predicted prevalence map by Ordinary Kriging
Decrease in model prediction error with increase in number of trees
Variable importance plot
Predicted median prevalence map by Random Forest
Bayesian equation for parameter estimation
geoRglmTraceplot for parameters estimated
Triangulated Mesh for Ethiopian prevalence data
| X | mean | sd | X0.025quant | X0.5quant | X0.975quant |
|---|---|---|---|---|---|
| b0 | -10.7019100 | 27.5594618 | -65.0088243 | -10.6591201 | 43.3169943 |
| altitude | 0.0047719 | 0.0035015 | -0.0018561 | 0.0046585 | 0.0120808 |
| isothermality | -0.4135565 | 0.2971715 | -1.0255053 | -0.4054335 | 0.1500352 |
| temp.season | -0.0079408 | 0.0059171 | -0.0208241 | -0.0075787 | 0.0027440 |
| annual.precp | 0.0134181 | 0.0043420 | 0.0053399 | 0.0132497 | 0.0225356 |
| annual.temp | 0.1110413 | 0.0695194 | -0.0163252 | 0.1076159 | 0.2587491 |
| popden | -0.2375209 | 0.0287466 | -0.2967719 | -0.2368306 | -0.1822866 |
Predicted median prevalence map by Random Forest